Identification of functionally connected multi-omic biomarkers for Alzheimer's disease using modularity-constrained Lasso.

ABSTRACT: Large-scale genome wide association studies (GWASs) have led to discovery of many genetic risk factors in Alzheimer's disease (AD), such as APOE, TOMM40 and CLU. Despite the significant progress, it remains a major challenge to functionally validate these genetic findings and translate them into targetable mechanisms. Integration of multiple types of molecular data is increasingly used to address this problem. In this paper, we proposed a modularity-constrained Lasso model to jointly analyze the genotype, gene expression and protein expression data for discovery of functionally connected multi-omic biomarkers in AD. With a prior network capturing the functional relationship between SNPs, genes and proteins, the newly introduced penalty term maximizes the global modularity of the subnetwork

SUBMITTER: Xie L 

PROVIDER: S-EPMC7299377 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature